Revisiting the FDI–Growth Nexus: ARDL Bound Test for BRICS Standalone Economies
Bibliographic record
Abstract
In this paper the author tests for the short-run dynamics and long-run cointegration relationship between foreign direct investment (FDI) inflows and economic growth for the BRICS (Brazil, Russia, India, China, and South Africa) standalone economies controlling for real exchange rate, trade openness, and domestic investment. The autoregressive distributed lag (ARDL) bounds testing method of cointegration is used to test for the long-run relationship of our FDI time series model by investigating annual macroeconomic datasets for the years 1981 to 2018 (inclusive). Coupled with the ARDL, the error correction model is applied to test for the short-run dynamics, while the Toda Yamamoto test is used to examine the causality direction between the constructs of interest. The Breusch-Godfrey and Ljung-Box are used as diagnostic tests for the ARDL assumptions of normality, independency, and autocorrelation in residuals, while the Breusch-Pagan-Godfrey test is used to test for heteroscedasticity. According to the short-run estimates, all variables have a significant lagged impact on FDI inflows with slight differences among countries. As for the long run, estimates reveal a positive and significant impact of GDP on FDI inflows for Russia, India, China, and South Africa but a positive and insignificant relationship for Brazil. The long-run estimates for the controlling variables evidence varied results among the BRICS countries. In contrast to Brazil and Russia, the Toda Yamamoto causality test discloses a significant and unidirectional flow between the GDP growth and FDI inflows for India, China, and South Africa. The results have meaningful implications for policy reform structures, economic integration among economies, multinational firms, and portfolio managers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".